The present invention relates generally to the field of database management, and more particularly to automatic or semi-automatic methods using statistical models to identify problematic database configurations.
Large information technology (IT) environments maintain hundreds of databases to support their business. Incidents associated with these databases result in losses of revenue, rework to recover and restore the data, and information loss that hurts business. In this context, problems inherent to database configurations are known to be challenging, owing to the large number of parameters involved (typically hundreds).
Solutions have been developed for self-optimizing/self-configuring Database Management Systems (DBMS s), which can be essentially classified into two categories, namely: the physical or logical tuning of DBMS s; and the tuning of database configuration parameters. In the context of physical or logical tuning, automatic index tuning has received most of the attention. In configuration parameter analysis, attempts have been made in finding optimal values for subsets of configuration parameters or even for only one configuration parameter.